44 citations · 52 across the 17 of their papers we have counts for
17 papers
What Makes a Good Medical Image Tokenizer? Rethinking Reconstruction and Generation in Medical Image Tokenization
Niklas Bubeck, Yundi Zhang, Vasiliki Sideri-Lampretsa +4
Latent diffusion models now dominate medical image generation, and every such pipeline rests on a \emph{tokenizer} that compresses images into the latent codes for image generation…
What Cohort INRs Encode and Where to Freeze Them
Vasiliki Sideri-Lampretsa, Sophie Starck, Robbie Holland +2
Reusing the early layers of cohort-trained INRs as initialization for new signals has been shown to accelerate and improve signal fitting, yet it remains unclear which layers of th…
Sparse Autoencoders for Interpretable Medical Image Representation Learning
Philipp Wesp, Robbie Holland, Vasiliki Sideri-Lampretsa +1
Vision foundation models (FMs) achieve state-of-the-art performance in medical imaging. However, they encode information in abstract latent representations that clinicians cannot i…
CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain
Maik Dannecker, Vasiliki Sideri-Lampretsa, Sophie Starck +5
Magnetic resonance imaging of fetal and neonatal brains reveals rapid neurodevelopment marked by substantial anatomical changes unfolding within days. Studying this critical stage…
Evaluation of Deformable Image Registration under Alignment-Regularity Trade-off
Vasiliki Sideri-Lampretsa, Daniel Rueckert, Huaqi Qiu
Evaluating deformable image registration (DIR) is challenging due to the inherent trade-off between achieving high alignment accuracy and maintaining deformation regularity. Howeve…
Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
Fabian Drexel, Vasiliki Sideri-Lampretsa, Henriette Bast +6
Dark-field radiography of the human chest has been demonstrated to have promising potential for the analysis of the lung microstructure and the diagnosis of respiratory diseases. H…
From Model Based to Learned Regularization in Medical Image Registration: A Comprehensive Review
Anna Reithmeir, Veronika Spieker, Vasiliki Sideri-Lampretsa +3
Image registration is fundamental in medical imaging applications, such as disease progression analysis or radiation therapy planning. The primary objective of image registration i…
Interpretable deformable image registration: A geometric deep learning perspective
Vasiliki Sideri-Lampretsa, Nil Stolt-Ansó, Huaqi Qiu +4
Deformable image registration poses a challenging problem where, unlike most deep learning tasks, a complex relationship between multiple coordinate systems has to be considered. A…
General Vision Encoder Features as Guidance in Medical Image Registration
Fryderyk Kögl, Anna Reithmeir, Vasiliki Sideri-Lampretsa +5
General vision encoders like DINOv2 and SAM have recently transformed computer vision. Even though they are trained on natural images, such encoder models have excelled in medical…
Intensity-based 3D motion correction for cardiac MR images
Nil Stolt-Ansó, Vasiliki Sideri-Lampretsa, Maik Dannecker +1
Cardiac magnetic resonance (CMR) image acquisition requires subjects to hold their breath while 2D cine images are acquired. This process assumes that the heart remains in the same…
Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases
Sophie Starck, Vasiliki Sideri-Lampretsa, Bernhard Kainz +3
Anatomical atlases are widely used for population studies and analysis. Conditional atlases target a specific sub-population defined via certain conditions, such as demographics or…
MAD: Modality Agnostic Distance Measure for Image Registration
Vasiliki Sideri-Lampretsa, Veronika A. Zimmer, Huaqi Qiu +2
Multi-modal image registration is a crucial pre-processing step in many medical applications. However, it is a challenging task due to the complex intensity relationships between d…
Using UK Biobank data to establish population-specific atlases from whole body MRI
Sophie Starck, Vasiliki Sideri-Lampretsa, Jessica J. M. Ritter +4
Reliable reference data in medical imaging is largely unavailable. Developing tools that allow for the comparison of individual patient data to reference data has a high potential…
Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation
Robert Graf, Joachim Schmitt, Sarah Schlaeger +8
Background: Automated segmentation of spinal MR images plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures presents ch…
Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations
Julian McGinnis, Suprosanna Shit, Hongwei Bran Li +9
Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to…
Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning
Walter A. Simson, Magdalini Paschali, Vasiliki Sideri-Lampretsa +2
Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians with diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound spe…
Multi-modal unsupervised brain image registration using edge maps
Vasiliki Sideri-Lampretsa, Georgios Kaissis, Daniel Rueckert
Diffeomorphic deformable multi-modal image registration is a challenging task which aims to bring images acquired by different modalities to the same coordinate space and at the sa…